Rapidfuzz

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Performance
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* further improve bitparallel implementation of uniform Levenshtein distance for strings with a length > 64 (in many cases more than 50% faster)

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Changed
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* add more benchmarks to documentation

Performance
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* add bitparallel implementation to InDel Distance (Levenshtein with the weights 1,1,2) for strings with a length > 64
* improve bitparallel implementation of uniform Levenshtein distance for strings with a length > 64
* use the InDel Distance and uniform Levenshtein distance in more cases instead of the generic implementation
* Directly use the Levenshtein implementation in C++ instead of using it through Python in process.*

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Fixed
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* Fix reference counting in process.extract (see 81)

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Fixed
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* Fix result conversion in process.extract (see 79)

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Changed
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* string_metric.normalized_levenshtein supports now all weights
* when different weights are used for Insertion and Deletion the strings are not swapped inside the Levenshtein implementation anymore. So different weights for Insertion and Deletion are now supported.
* replace C++ implementation with a Cython implementation. This has the following advantages:

* The implementation is less error prone, since a lot of the complex things are done by Cython
* slightly faster than the current implementation (up to 10% for some parts)
* about 33% smaller binary size
* reduced compile time

* Added \*\*kwargs argument to process.extract/extractOne/extract_iter that is passed to the scorer
* Add max argument to hamming distance
* Add support for whole Unicode range to utils.default_process

Performance
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* replaced Wagner Fischer usage in the normal Levenshtein distance with a bitparallel implementation

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Fixed
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* The bitparallel LCS algorithm in fuzz.partial_ratio did not find the longest common substring properly in some cases.
The old algorithm is used again until this bug is fixed.